Mortality rates and risk factors after low-trauma hip fracture in the largest university center in Romania

Predictors of mortality for patients with hip fracture: a study of the nationwide 1-year records of 24,950 fractures in Romania

We conducted a study evaluating incidence rates and influencing factors in Romanian hip fracture patients. Our results showed that the type of fracture and its respective surgical procedure as well as hospital characteristics correlate with mortality. Updated incidences can result in updated treatment guidelines.

PURPOSE

The aim of our study was to assess incidence rates for a revision-calibration of the Romanian FRAX tool and to evaluate particularities of hip fracture cases to determine patient- and hospital-related variables affecting mortality.

METHODS

We conducted a retrospective study using hospital reports of hip fracture codes to the National School of Statistics (NSS) from January 1, 2019, until December 31, 2019. The study population included 24,950 patients presenting to Romanian public hospitals in all 41 counties: ≥ the age of 40 with diagnostic ICD 10 codes: S72.0 femoral neck fracture, S72.1 pertrochanteric femoral fracture, and S72.2 subtrochanteric femoral fracture and procedure codes: O11104 (trochanteric/sub capital internal fixation), O12101 (hemiarthroplasty), O11808 (closed femoral reduction with internal fixation), O12103 (partial arthroplasty), O12104 (total arthroplasty). Hospital length of stay (LoS) was classified as follows: < 6, 6-9, 10-14, and ≥ 15 days.

RESULTS

Incidence of hip fractures was 248/100,000 among those aged 50 + and 184/100,000 within the 40 + age category. Average age of the patients was 77 years (80 for females, 71 for males); 83.7% of the patients were 65 + with equivalent urban-rural distributions. Males had a 1.7 times higher mortality risk. Each year increase in age added a mortality risk of 6.9%. In-hospital mortality was 1.34 times higher among patients living in urban areas. Hemiarthroplasty and partial/total unilateral/bilateral arthroplasty had a lower risk of mortality than trochanteric/sub capital internal fixation (p < 0.02, p < 0.033).

CONCLUSION

Gender, age, residence, and procedure type had significant impact on mortality. Updated incidence rates will allow the revision of Romania's FRAX model.

Bimetallic Metal-Organic Framework for Mitigating Aseptic Osteolysis

The disability rate of joint diseases can be reduced by the use of artificial joints, but joint loosening at a late state limits the lifespan and surgical efficacy of the joints. Wear particles can be recognized by macrophages and induce cells to produce reactive oxygen species (ROS) and inflammatory factors, causing persistent inflammation and decreased osteogenic activity, which ultimately leads to loosening of joint prostheses. Here, the platinum (Pt) nanozymes with excellent ROS scavenging and anti-inflammatory capabilities were encapsulated in zinc imidazolium zeolite framework-8 (ZIF-8), and then the osteogenic active element lanthanum (La) was introduced through ion exchange to finally construct a bimetallic metal-organic framework (Pt@ZIF-8@La). In vitro and in vivo experiments demonstrated that this multifunctional nanoplatform possessed the functions of efficient scavenging of ROS, immune regulation, and promotion of osteogenic differentiation. Meanwhile, the mechanism is explored that Pt@ZIF-8@La can also promote osteogenic mineralization by upregulating the ratio of the osteoprotegerin (OPG)/receptor activator of the NF-κB ligand (RANKL), which can achieve a synergistic therapeutic effect of immunomodulation and osteogenesis, thereby realizing the purpose of relieving aseptic osteolysis.

Identification of risk factors for 1-year mortality among critically ill older adults with hip fractures surgery: A single medical center retrospective study

Aim

The purpose of this study was to analyze the potential risk factors for mortality 1 year after hip fracture surgery in critically ill older adults.

Methods

We reviewed 591 critically ill older adults who underwent hip surgery at our institution from January 2018 to April 2021. We collected baseline demographics, clinical information, and 1-year survival status of the sample patients by means of medical record systems and follow-up phone calls. Patients were divided into survival and mortality groups based on survival within 1 year after surgery.

Results

Based on the results of the 1-year postoperative follow-up of patients, we obtained 117 cases in the death group and 474 cases in the survival group, and this led to a 1-year mortality rate of 19.8% (117/591) after hip fracture in critically ill older adults at our hospital. The risk factors that influenced the 1-year postoperative mortality were identified as advanced age (HR:1.04, 95%, 1.01–1.06), preoperative arrhythmia (HR: 1.95, 95%, 1.26–2.70), high level of NLR (HR:1.03, 95%, 1.01–1.06), respiratory failure (HR: 2.63, 95%, 1.32–5.23), and acute cardiovascular failure. 5.23) and acute cardiovascular events (HR: 1.65, 95%, 1.05–2.59).

Conclusion

Advanced age, preoperative arrhythmias, high levels of NLR, postoperative respiratory failure, and acute cardiovascular events were independent risk factors for survival of critically ill older adults with hip fracture at 1 year after surgery. Therefore, laboratory tests such as high levels of preoperative NLR can be an important indicator of patient prognosis.

The Role of Smoking and Body Mass Index in Mortality Risk Assessment for Geriatric Hip Fracture Patients

Background

Smoking, obesity, and being below a healthy body weight are known to increase all-cause mortality rates and are considered modifiable risk factors. The purpose of this study is to assess whether adding these risk factors to a validated geriatric inpatient mortality risk tool will improve the predictive capacity for hip fracture patients. We hypothesize that the predictive capacity of the Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) tool will improve.

Methodology

Between October 2014 and August 2021, 2,421 patients >55-years-old treated for hip fractures caused by low-energy mechanisms were analyzed for demographics, injury details, hospital quality measures, and mortality. Smoking status was recorded as a current every-day smoker, former smoker, or never smoker. Smokers (current and former) were compared to non-smokers (never smokers). Body mass index (BMI) was defined as underweight (<18.5 kg/m²), healthy weight (18.5-24.9 kg/m²), overweight (25.0-24.9 kg/m²), or obese (>30 kg/m²). The baseline STTGMA tool for hip fractures (STTGMAHIP_FX_SCORE) was modified to include patients’ BMI and smoking status (STTGMA_MODIFIABLE), and new mortality risk scores were calculated. Each model’s predictive ability was compared using DeLong’s test by analyzing the area under the receiver operating curves (AUROCs). Comparative analyses were conducted on each risk quartile.

Results

A comparison of smokers versus non-smokers demonstrated that smokers experienced higher rates of inpatient (p = 0.025) and 30-day (p = 0.048) mortality, myocardial infarction (p < 0.01), acute respiratory failure (p < 0.01), and a longer length of stay (p = 0.014). Comparison among BMI cohorts demonstrated that underweight patients experienced higher rates of pneumonia (p = 0.033), decubitus ulcers (p = 0.046), and the need for an intensive care unit (ICU) (p < 0.01). AUROC comparison demonstrated that STTGMA_MODIFIABLE significantly improved the predictive capacity for inpatient mortality compared to STTGMAHIP_FX_SCORE (0.792 vs. 0.672, p = 0.0445). Quartile stratification demonstrated the highest risk cohort had a longer length of stay (p < 0.01), higher rates of inpatient (p < 0.01) and 30-day mortality (p < 0.01), and need for an ICU (p < 0.01) compared to the minimal risk cohort. Patients in the lowest risk quartile were most likely to be discharged home (p < 0.01).

Conclusions

Smoking, obesity, and being below a healthy body weight increase the risk of perioperative complications and poor outcomes. Including smoking and BMI improves the STTGMAHIP_FX_SCORE tool to predict mortality and risk stratify patient outcomes. Because smoking, obesity, and being below a healthy body weight are modifiable patient factors, providers can counsel patients and implement lifestyle changes to potentially decrease their risk of longer-term poor outcomes, especially in the setting of another fracture. For patients who are former smokers, providers can use this information to encourage continued restraint and healthy choices.

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

Background

Post-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients.

Methods

This retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model.

Results

Feature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively.

Conclusions

Compared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year.